# Ethics in the Age of AI: An Analysis of AI Practitioners' Awareness and Challenges

arXiv: https://arxiv.org/abs/2307.10057 · HTML: https://mlsanity.com/paper/2307.10057/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 81 references that are in the map.
- First posted on arXiv 14 Jul 2023.

## Builds on (most cited first, 20 of 20)

- [Improving fairness in machine learning systems: What do industry practitioners need?](https://mlsanity.com/paper/1812.05239/) (2018)
- [Principles alone cannot guarantee ethical AI](https://mlsanity.com/paper/1906.06668/) (2019)
- [The Ethics of AI Ethics -- An Evaluation of Guidelines](https://mlsanity.com/paper/1903.03425/) (2019)
- [Assessing Gender Bias in Machine Translation -- A Case Study with Google Translate](https://mlsanity.com/paper/1809.02208/) (2018)
- [Artificial Intelligence: the global landscape of ethics guidelines](https://mlsanity.com/paper/1906.11668/) (2019)
- [Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making](https://mlsanity.com/paper/1802.01029/) (2018)
- [Assessing the Fairness of AI Systems: AI Practitioners' Processes, Challenges, and Needs for Support](https://mlsanity.com/paper/2112.05675/) (2021)
- [Sampling in Software Engineering Research: A Critical Review and Guidelines](https://mlsanity.com/paper/2002.07764/) (2020)
- [Ethics as a service: a pragmatic operationalisation of AI Ethics](https://mlsanity.com/paper/2102.09364/) (2021)
- [Diversity and Inclusion in Artificial Intelligence](https://mlsanity.com/paper/2305.12728/) (2023)
- [AI Ethics Principles in Practice: Perspectives of Designers and Developers](https://mlsanity.com/paper/2112.07467/) (2021)
- [Biased Programmers? Or Biased Data? A Field Experiment in Operationalizing AI Ethics](https://mlsanity.com/paper/2012.02394/) (2020)
- [ECCOLA -- a Method for Implementing Ethically Aligned AI Systems](https://mlsanity.com/paper/2004.08377/) (2020)
- [Practitioners' Perceptions of the Goals and Visual Explanations of Defect Prediction Models](https://mlsanity.com/paper/2102.12007/) (2021)
- [Assessing Demographic Bias Transfer from Dataset to Model: A Case Study in Facial Expression Recognition](https://mlsanity.com/paper/2205.10049/) (2022)
- [Ethically Aligned Design of Autonomous Systems: Industry viewpoint and an empirical study](https://mlsanity.com/paper/1906.07946/) (2019)
- [Socio-Technical Grounded Theory for Software Engineering](https://mlsanity.com/paper/2103.14235/) (2021)
- [The Key Concepts of Ethics of Artificial Intelligence - A Keyword based Systematic Mapping Study](https://mlsanity.com/paper/1809.07027/) (2018)
- [Ethically Aligned Design: An empirical evaluation of the RESOLVEDD-strategy in Software and Systems development context](https://mlsanity.com/paper/1905.06417/) (2019)
- [Time for AI (Ethics) Maturity Model Is Now](https://mlsanity.com/paper/2101.12701/) (2021)

## Built on by (most cited first, 6 of 6)

- [Beyond Words: On Large Language Models Actionability in Mission-Critical Risk Analysis](https://mlsanity.com/paper/2406.10273/) (2024)
- [Privy: Envisioning and Mitigating Privacy Risks for Consumer-facing AI Product Concepts](https://mlsanity.com/paper/2509.23525/) (2025)
- [Walking the Tightrope of LLMs for Software Development: A Practitioners' Perspective](https://mlsanity.com/paper/2511.06428/) (2025)
- [Navigating Fairness: Practitioners' Understanding, Challenges, and Strategies in AI/ML Development](https://mlsanity.com/paper/2403.15481/) (2024)
- [RobEthiChor: Automated Context-aware Ethics-based Negotiation for Autonomous Robots](https://mlsanity.com/paper/2507.22664/) (2025)
- [The Perils of Agency: How Developers Perceive, Prioritize, and Address Risks in Agentic AI Products](https://mlsanity.com/paper/2606.15485/) (2026)

## Abstract (arXiv)

Ethics in AI has become a debated topic of public and expert discourse in recent years. But what do people who build AI - AI practitioners - have to say about their understanding of AI ethics and the challenges associated with incorporating it in the AI-based systems they develop? Understanding AI practitioners' views on AI ethics is important as they are the ones closest to the AI systems and can bring about changes and improvements. We conducted a survey aimed at understanding AI practitioners' awareness of AI ethics and their challenges in incorporating ethics. Based on 100 AI practitioners' responses, our findings indicate that majority of AI practitioners had a reasonable familiarity with the concept of AI ethics, primarily due to workplace rules and policies. Privacy protection and security was the ethical principle that majority of them were aware of. Formal education/training was considered somewhat helpful in preparing practitioners to incorporate AI ethics. The challenges that AI practitioners faced in the development of ethical AI-based systems included (i) general challenges, (ii) technology-related challenges and (iii) human-related challenges. We also identified areas needing further investigation and provided recommendations to assist AI practitioners and companies in incorporating ethics into AI development.